Microprocessor-based electrical power systems accumulate considerable amounts of information concerning the electrical distribution systems to which they are connected, as well as the power equipment itself. Today's utility monitoring systems provide end-users with the capability to remotely monitor a variety of equipment via automatic monitoring devices. The spectral information is used when compensating a system to reduce harmonic content and for other troubleshooting purposes.
The typical monitoring device such as a digital power meter utilizes an analog-to-digital (A/D) converter and a microprocessor, and thus all analysis is done in the discrete time or digital domain. The input signals (such as current or voltage) are digitized by the A/D converter operating at a sampling rate which is determined by an adjustable frequency digital clock. In order to sample an integer number of cycles of the signal (assuming the sample rate is held constant during the sampling window), it is necessary that the sample rate and the frequency of the signal be integrally related. The required sample rate is determined by measuring the frequency of the input signal and then multiplying the frequency by some integer such that the Nyquist requirement and other system constraints are met. Because the adjustable sample clock does not have infinite precision, it is not possible to set it to the required frequency for some input frequencies.
The fundamental period of the system voltage may be measured by identifying zero crossings of the waveform of the power signal, and determining the time interval between those zero crossings. The reciprocal of the fundamental period is the measured frequency, and the desired sampling frequency is a multiple of that measured frequency. However, the actual sampling frequency is controlled by an adjustable-frequency digital clock, which is not infinitely variable and cannot be easily controlled with the requisite degree of precision to achieve an actual sampling frequency within the maximum permissible error (e.g., ±0.03%).
There are primarily two methods used to sample the voltages and currents in a power system in order to make power/energy measurements and power quality measurements (harmonics, etc.) One is to operate the analog to digital (A/D) converter at a constant sample rate and window the voltage and current samples so as to mitigate to some extent the effects of non-synchronous sampling. These effects consist primarily of inaccurate RMS values (and thus inaccurate power and energy) and inaccurate harmonic measurements at certain frequencies relative to the sample rate. The other method is to synchronously sample the inputs by using hardware or firmware to control the sampling. The windowing method requires that each sample on each voltage and current channel is multiplied by the window function value at that sample index. This requires significant processor bandwidth and memory capacity especially as the sample rate increases. Synchronous sampling may be achieved using either hardware or firmware. Controlling the sampling in hardware usually adds cost when compared to the firmware solution unless the load on the processor requires a more expensive processor.
There are two ways to achieve synchronous sampling through the use of firmware. One is for the processor to control the sample rate of the A/D converter. The other is to operate the A/D converter at a constant sample rate and re-sample the data in firmware. For example, hardware control of the sample rate in certain devices is dictated by the use of an A/D converter such as a sigma delta type of A/D converter and the sample clock must operate between approximately 1 and 4 MHz. Thus, it is difficult to obtain high precision control of the clock through firmware.
Using a sigma delta analog to digital converter to provide synchronous sampling proves to be significantly more complex than using successive approximation (SAR) analog to digital converters. Digital clock timers do not allow for infinite sampling interval resolution. The resulting resolution is relatively poor and the error increases depending on the magnitude of the fractional portion of the ratio of the sample frequency to the power frequency. Thus, it would be useful to have an accurate sampling protocol that provides synchronous sampling while efficiently using computing and memory resources.